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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/6621

    Title: 應用資料探勘於校正適應性號誌決策邏輯的車流偵測資料之研究
    Authors: 徐國鈞
    Keywords: 資料探勘
    Data Mining
    adaptive signal control
    Date: 2005-07-31
    Issue Date: 2009-12-30 16:51:12 (UTC+8)
    Abstract: 適應性號誌控制系統由於需要較多偵測器來掌握臨近路段之車流資訊,對於交通資訊精確度之要求也較動態號誌系統為高,因此自模式研發以來,在實務上常因建置與維護成本的考量下,一直未能被大量採行;因此不論控制區域的範圍,如何將偵測器的數量減至最少,但確能夠維持相同的決策品質,是目前適應性號誌控制系統實用化的關鍵。另一方面,隨著資訊科技的快速成長,資訊系統在巨量資料處理和高速運算等功能上,近年來有非常顯著的突破與進步。資料探勘(Data Mining)亦被稱為資料採礦作業即藉由此類優異的資料處理與運算能力,在所掌握到的大量資料中進行多維度之搜尋,並更進一步地擷取出藏匿於其中的知識樣模(Knowledge Patterns)。換言之,資料探勘所能提供的智慧型資料分析,將可讓吾人透過對資料內涵的更通透瞭解,進而充份且有效地解決所面對的各種問題。本研究將以國內長期發展的COMDYCS-Ⅲ模適應性號誌控制式為基礎,以系統模擬的方法的得到偵測器所傳送之車流資料,並應用資料探勘的方法,從控制範圍的不同偵測器資料中,找出資料間的時空相關特性來校估車流推估模式。
    Due to the rapid development of ITS in the world recently, for traffic signal control system in the urban cities, it is possible to gain sufficient road traffic information from multiple approaches, such as vehicle detectors on the roads and Global Position with In-Vehicle Information Systems. This can lead to analytical calculation of per vehicle per second basis. At the same time, traffic adaptive signal control system can react to accidents and thus satisfy the demand on constantly enhancing the traffic excellence among the general public. It will also replace the traditional ways of fixed hour and dynamic control and become the popular traffic signal control system. This research aims to apply the theory of applied data mining to make an attempt to revolve the traffic prediction of traffic adaptive signal control system. As for the model adopted in this research, it is based on the viewpoint of global characteristics. The outcome of this research can solve the bottlenecks of development of traffic adaptive signal control system at present, and therefore, has potential for further development and promotion.
    Appears in Collections:[資訊管理系所] 研究計畫

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